Editorial: the March 2022 cover paper

Journal of Materials Science -     

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

The structure,vacancy characteristics,and magnetic properties of GdMn1-xCrxO3 ceramics

To investigate the evolution of the structural and enhanced magnetic properties of GdMnO₃ systems induced by the substitution of Mn with Cr, polycrystalline GdMn_1-xCr_xO₃ samples were synthesized via solid-state reactions. XRD characterization shows that all GdMn_1-xCr_xO₃ compounds with single-phase structures crystallize well and that Cr³⁺ ions entering the lattice sites of GdMnO₃ induce structural distortion. SEM results indicate that the grain size of the synthesized samples (a few microns) decreases as the Cr substitution concentration increases. Positron annihilation lifetime spectroscopy reveals that vacancy-type defects occur in GdMn_1-xCr_xO₃ ceramics and that the vacancy size and concentration clearly change with the Cr content. The temperature and field dependence of the magnetization curves show that Cr substitution significantly influences the magnetic ordering of the gadolinium sublattice, improving the weak ferromagnetic transition temperature and magnetization of GdMn_1-xCr_xO₃. The enhanced magnetization of GdMn_1-xCr_xO₃ is closely related to the vacancy defect concentration.     

Research and development of liquid hydrogen (LH2) temperature monitoring system for marine applications

Monitoring the temperature in liquid hydrogen (LH₂) storage tanks on ships is important for the safety of maritime navigation. In addition, accurate temperature measurement is also required for commercial transactions. Temperature and pressure define the density of liquid hydrogen, which is directly linked to trading interests. In this study, we developed and tested a liquid hydrogen temperature monitoring system that uses platinum resistance sensors with a nominal electrical resistance of approximately 1000 Ω at room temperature, PT-1000, for marine applications. The temperature measurements were carried out using a newly developed temperature monitoring system under different pressure conditions. The measured values are compared with a calibrated reference PT-1000 resistance thermometer. We confirm a measurement accuracy of ±50 mK in a pressure range of 0.1 MPa–0.5 MPa.     

Full AC analysis of value of DER to the grid and optimal DER scheduling

This paper presents part of the work ComEd and Quanta Technology have performed to quantify the locational and temporal value of DER to avoid distribution grid upgrade investments. It focuses on the formulation of a robust and efficient algorithm for DER optimal dispatch on a distribution feeder to mitigate the violation of current and voltage limits using the allocated cost of capacity and locational marginal value of real and reactive DER injection/withdrawal.     

Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.